Description

This
course introduces students to research in spoken language in computational
linguistics, aka natural language processing (NLP). We will study the different
`meanings' that can be conveyed by the way that
speakers produce sentences, techniques for analyzing spoken language, methods
of developing speech technologies such as text-to-speech systems and speech
recognition systems, and applications of speech technologies in the real world,
such as spoken dialogue systems (SDS). Students will build an SDS in a
domain of their choice, working in small teams. NB: This course
can be counted as a PhD elective in Advanced AI. It is a requirement for
the MS NLP Track. There are no official prerequisites for this course
except Data Structures or equivalent, and no prior knowledge of NLP will be
assumed.

Requirements

The major
requirements of the course are a midterm, a final, and a 3-part class project.
Class participation will also contribute to your final grade. The project
involves building a
spoken dialogue system in a domain of your choice. You will build a text-to-speech
(TTS) system and an automatic speech recognition (ASR) system from components we will
provide; the dialogue component will involve building a simple system to put
inputs and outputs together to accomplish some interesting and useful or fun
task. You are encouraged to do these projects in teams of 2-3. There
will be several project deadlines during the term where we evaluate your project
description, your TTS system, your ASR system, and the overall project.
Project deadlines will be allowed total of 5 late days with no questions asked; after that, 10% per late day will be
deducted from the grade for that component, unless you have a note from your
doctor. Do not
use these up early! Save them for real emergencies.

All
students are required to have a Computer
Science Account for this class. To sign up for one, go to the
CRF website and then click on
"Apply for an Account". The
Speech Lab is available
for use in homeworks as needed on a signup basis.

Some parts of the
project must be done in the Lab.

Academic Integrity

Copying or paraphrasing someone's work (code included),
or permitting your own work to be copied or paraphrased, even if only in part,
is not allowed, and will result in an automatic grade of 0 for the entire
assignment or exam in which the copying or paraphrasing was done. Your grade
should reflect your own work. If you believe you are going to have trouble
completing an assignment, please talk to Prof. Hirschberg in
advance of the due date. Please see the
university policy.

Required
texts:

Daniel Jurafsky
and James H. Martin
Speech and Language
Processing (second edition). Pearson: Prentice Hall. 2009. See
errata
before you do each reading assignment. There are some typos in
algorithms.

Other required readings
are available online via links from this syllabus.

Grading:

50% Exams

50% Course Project

Class participation will be taken into account in calculating the final
grade.